1. Field
Embodiments relate to a method, apparatus, and medium for estimating a pose of a mobile robot using a particle filter, and more particularly, to a method, apparatus, and medium for estimating a pose of a mobile robot using a particle filter that is capable of accurately estimating the pose of the mobile robot by reflecting an error of a sensor in a grid-based simultaneous localization and map building (SLAM) algorithm using a particle filter and adjusting a weight.
2. Description of the Related Art
In recent years, in addition to robots for industrial use, robots for household use and robots for office services have been put to practical use. For example, there are cleaning robots, guiding robots, and security robots.
The robots perform functions on their own in a predetermined environment while moving within a predetermined area. In order that each of the robots moves and performs the function on its own in the predetermined environment, the robot needs to build a map of the surrounding space around the robot and information on the position and a heading angle of the robot. A grid-based simultaneous localization and mapping (SLAM) algorithm may be used as a method of building the map and recognizing the position of the robot.
According to the SLAM algorithm, a map of the surrounding area at a predetermined position is built, and on the basis of the built map, the position of the robot, where it has moved, is determined. These processes are repeated to thereby estimate the position of the robot and the map of the surroundings.
In the SLAM algorithm, a particle filter technique may be used to estimate the position of the robot itself. The particle filter technique is a method that is used to estimate the optimum pose of the robot by extracting a plurality of samples that predict the position and the head angle of the robot, and using the probability that each of the samples has an actual position and heading angle of the robot. The term “pose” in the invention indicates a two-dimensional coordinate position on the plane of the mobile robot and a heading angle of the mobile robot.
In a grid-based SLAM using range data, a map can be built by using all of the range data without extracting feature points. However, in the related art, a range finder (hereinafter, referred to as a “sensor”) that has high accuracy needs to be used to obtain the range data. Therefore, the accuracy of the sensor is very important to obtain a good quality map.
For the robots for household use or the robots for business services, such as the cleaning robots and the security robots, which are put to practical use, even if the sensor is cheap and has relatively low accuracy, it is still necessary to build a map and accurately estimate the position of the robot.